The FXM, as of May 2004

1. The FXM, as of May 2004 An auspicious beginning . . .

2. The FXM, as of May 2004 but . . . Knotty problems abound

3. We can (and do) congratulate ourselves for . . . New cell, new experimental conditions Parts list and network map Primary assays: Ca2+, p-Akt, PH-Akt . . . in cell populations and single cells RNAi works, at (relatively) high throughput

4. Many KDs produce stable Ca2+ response phenotypes; of these, many are not expected Throughput: prepare and assay 4 lines per week RNAi Knockdowns (KDs) so far 37 KD lines, against 31 targets KD robust: >99% in one third, >90% in two thirds, >80% in almost all *KDs of 28 targets change ~30 % of Ca2+ responses to FXM ligands

5. FXM: Challenges, questions Weak IgG2a responses KDs without phenotypes Need to validate RNAi phenotypes Need more/better assays for network intermediates Modeling is just beginning

6. Multiple KD phenotypes: delight vs. disaster Many phenotypes are unexpected, often with gain of function rather than loss Are we . . . Heaven? Hell? Uncovering unsuspected complexity and generating fascinating puzzles? or Opening a Pandora’s box of misleading, biologically irrelevant phenomena?

7. What are other sources of variability? How should we deal with them? Validating knockdowns: the questions Are shRNAi KDs reliable, in general and in individual cell lines? Can an shRNAi exert off-target effects? Are we selecting clones with compensatory mutations or long-term adaptations? (Do we want to study such adaptations?) What should we do about any/all of these?

8. Validating knockdowns: compensatory mutations/adaptations To make such compensations less likely, knock down the target faster . . . Antisense RNA vs. the same target Transiently transfect siRNA vs. the same target and/or Replicate the phenotype with a KD down- stream (to rule out compensation at sites between the first and second targets)

9. Validating knockdowns: coping with variability Early days! . . . We don’t know yet how much variation to expect, from any/all sources Initially, with several ‘unexpected’ phenotypes: Replicate cell lines with different shRNAi sequences (some already replicate the phenotype) Multiple determinations of responses, to assess general experimental variability mRNA arrays, antisense, siRNAi, as above Devise/apply better statistical criteria for comparing responses

10. Validating knockdowns: reverse the phenotype Express the target protein in the shRNAi, line, using a cDNA it cannot affect (e.g., human vs. mouse DNA sequence) Reversal of the shRNAi phenotype will indicate that the phenotype was indeed produced by KD of the target protein* *But will not rule out compensatory mutations/adaptations

11. Validating knockdowns: test a good hypothesis (What we always want, of course!) An shRNAi phenotype is more likely to be due to KD of the target protein if it is predictably affected by a second perturbation E.g., the PTEN KD* appears to increase the Ca2+ response to C5a Hypothesis 1: Effect is due to elevated PIP3 PI3K inhibitor should reverse Hypothesis 2: Elevated PIP3 increases Ca2+ response by targeting PLCg to membrane PLCg KD should reverse *Caution: Reproducibility of PTEN KD phenotype needs to be confirmed

12. Magical inductionism vs. needlepoint nihilism Get more data! Test more Hypotheses! From unbiased data the truth will accrue Data without ideas = ignorance ‘Creative tension’

13. Relieve your creative tension! In the AfCS Hypothesis center Each hypothesis will include . . . AfCS data Hypothesis One experiment that would disprove it

14. Phosphorylation disappointing: few, often not robust Plan/hope: SILAC, AQUApeptide technologies XFP translocations Screening under way Intermediate signals Pressing need to assay many more intermediate variables PIP3, IP3, DAG vexingly hard to measure Lipids, PIP3 FRET assays

15. Network models From the modelers we ask a lot Construct a model network that . . . Represents a comprehensive set of molecular interactions responsible for key responses Can vary strengths of interactions & activities, in silico, to simulate responses Predicts and evaluates responses in the cell Easily incorporates (& even suggests) new hypotheses (feedbacks, connections, nodes) Evaluates experimental tests of these new hypotheses

16. Network models The bad news A difficult task, likely to remain so Good precedents are rare, but not unknown The good news Will model responses of cell populations AND of single cells Abundant data kindles modelers’ enthusiasm

17. It’s a new day! Overcast . . . but full of promise

19. IgG2a responses This tyrosine-phosphorylation pathway makes an immensely attractive target to study, and . . . Ca2+ & p-Akt responses are quantitatively similar to C5a responses But: IgG2a elicits little detectable tyrosine phosphorylation (because Syk is poorly expressed?) Single cell responses are weak, not yet reproducible

20. IgG2a responses On the one hand . . . Signaling mechanisms differ from those of GPCR pathways Already see potentially interesting (& unexpected) shRNAi phenotypes But . . . How can we begin to understand an IgG2a- triggered network without measuring phosphotyrosine responses So Adapt more sensitive technology (SILAC or AQUApeptide?) And . . . ?

21. KDs without phenotypes E.g. IP3R KDs (so far) KD ineffective: assess by western, RT-PCR; try alternative shRNAi sequences Redundant isoforms: double (& ? triple) KDs with multiple lentiviruses Redundant signals: regulation predominantly by a different pathway (which we must find)

22. Validating knockdowns: off-target effects Can an shRNAi exert off-target effects? Probably yes, as already reported with siRNA But how frequently? In a specific cell line? To estimate how often this occurs . . . Immunoblots against unrelated target proteins mRNA arrays in multiple control vs. shRNAi-expressing lines

23. Intermediate signals We need to measure these to understand information flow through the network n i m Ca2+/ PIP3 Ligand j o h p k What will a KD at i, j, or k do to a signal transmitted at nodes n, o, or p?